Search results

Introduction: Sustainable human resource management (SHRM) as a practice is nowadays seen as an essential factor contributing to an individual’s and organisation’s growth. At the organisational level, the people concerned face many pressures to inculcate SHRM practices from various authorities and stakeholders.

Purpose: This chapter explains SHRM as a concept through an extensive literature review along with the evolutionary stages and multi-lateral perspectives of SHRM. The factors affecting this concept are Economic, Social, and Environmental; its driving forces like Employees, Government and Market Pressure; Employee Outcomes, namely Employee retention, satisfaction, motivation and Employee Presence; Organisational outcomes – Business level, Workers’ satisfaction, improved environmental outcomes better correlations, etc.; and value created by SHRM in terms of both employee and organisation are thereby explained.

Methodology: A specific procedure has been employed since the chapter has been based on literature review. The process of systematic literature review has been followed, which lays down the process followed by the authors – right from the Scope Formulation to the Illustration of Conceptual Framework.

Findings: A conceptual model is represented as a basis of the literature review, which the organisation can use and apply to develop SHRM practices, and finally, the precise effects of the research findings are suggested alongside ideas for future research.

The primary objective of this investigation was to explore how employees’ utilization of social media for work-related purposes impacts their service innovation behavior, both directly and through the intermediary mechanisms of knowledge management and employees’ risk-taking.

In developing its conceptual framework, this study has drawn upon the stimulus-organism-response (SOR) theory. To test its hypotheses, this study has surveyed 241 financial analysts from ten Iranian financial companies and has employed variance-based structural equation modeling (specifically, PLS-SEM) with the assistance of “WarpPLS 8.0 software.”

The findings revealed that employees’ work-related use of social media positively influences their service innovation behavior using knowledge management, encompassing knowledge sharing and acquisition capability as well as employee risk-taking. However, this influence is not directly significant.

To the best of our knowledge, this study marks the first instance in which the effect of work-related use of social media on employee service innovation behavior directly and through the mediating roles of knowledge management and risk-taking has been investigated through the lens of the SOR paradigm, especially in the financial sector.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

This study aims to show that employees' excessive work-related use of enterprise social networks (ESN) can be accompanied by some work-related strains, hindering them from continuing utilization of ESN at work. To this end, the impact of employees' excessive work-related utilization of ESN on their discontinuous usage intentions by mediating roles of employees' impression management concerns, privacy concerns and ESN fatigue will be evaluated.

Stimulus-organisms-response (S-O-R) framework has been drawn to support the design of this research. Using an entirely random data collection, 173 ESN users from 10 Iranian organizations were surveyed. The model was assessed using partial least squares structural equations modeling (PLS-SEM).

The results of the study confirm that employees' excessive work-related use of ESN positively affects impression management and privacy concerns, resulting in ESN fatigue. Furthermore, ESN fatigue plays a predicting role in ESN discontinuous usage intention.

According to the obtained results, if work-related use of ESN exceeds a normal threshold (i.e. excessive usage), employees will stop using ESN in their work due to the work-related strains delivered to them, revealing the dark side of ESN usage in organizations.

This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.

In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.

The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.

The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

Recently, work-related social media use (WSMU) in organisations and its association with employee outcomes have received considerable research attention. This study examines the association between WSMU, psychological well-being (PW) and innovative work performance (IP). In addition, it explores the mediating role of PW and the moderating role of fear of missing out (FoMO).

A sample of 233 employees working in different organisations was recruited from India to complete the survey. Structural equation modelling was applied to analyse the data.

The result reveals that WSMU has a positive and direct effect on IP. Moreover, the indirect effect via PW among the association was positive and significant. Furthermore, FoMO moderates the indirect relationship between WSMU and IP.

This research is a pioneering work that has contributed to the scarce literature by exploring the relationship between employees' social media use, PW and IP. This research has important theoretical and management contributions because it examines the impact of WSMU on IP, mediating role of PW and moderating role of FoMO among the association between WSMU and employee outcomes.